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      Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment

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          Abstract

          Globally, over 2 billion people suffer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a field trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhibited a significant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identified highly significantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively , compared to the corresponding single-trait-based models. The identified significantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortification of micronutrients via marker-assisted (MAS) and genomic selection (GS) methods.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00438-023-02074-6.

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          TASSEL: software for association mapping of complex traits in diverse samples.

          P J Bradbury, Z. Zhang, D. E. Kroon … (2007)
          Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.
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            Efficient methods to compute genomic predictions.

            P VanRaden (2008)
            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              Shifting the limits in wheat research and breeding using a fully annotated reference genome

              Zeev Frenkel, Song Weining, Tzion Fahima … (2019)
              An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.
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                Author and article information

                Contributors
                Admas Alemu:
                ORCID: http://orcid.org/0000-0001-7056-2699
                admas.alemu.abebe@slu.se
                Journal
                Journal ID (nlm-ta): Mol Genet Genomics
                Journal ID (iso-abbrev): Mol Genet Genomics
                Title: Molecular Genetics and Genomics
                Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )
                ISSN (Print): 1617-4615
                ISSN (Electronic): 1617-4623
                Publication date (Electronic): 18 October 2023
                Publication date PMC-release: 18 October 2023
                Publication date (Print): 2023
                Volume: 298
                Issue: 6
                Pages: 1515-1526
                Affiliations
                [1 ]The International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
                [2 ]GRID grid.501615.6, ISNI 0000 0004 6007 5493, AgroBioSciences, Mohammed VI Polytechnic University (UM6P), ; Ben Guerir, Morocco
                [3 ]Department of Plant Breeding, Swedish University of Agricultural Sciences, ( https://ror.org/02yy8x990) Alnarp, Sweden
                Author notes

                Communicated by Bing Yang.

                Author information
                http://orcid.org/0000-0001-7056-2699
                Article
                Publisher ID: 2074
                DOI: 10.1007/s00438-023-02074-6
                PMC ID: 10657311
                PubMed ID: 37851098
                SO-VID: 5f7ebe9d-5241-40e7-bd61-56e762e1c45b
                Copyright © © The Author(s) 2023
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 15 July 2023
                Date accepted : 28 September 2023
                Funding
                Funded by: Swedish University of Agricultural Sciences
                Open Access :

                Open access funding provided by Swedish University of Agricultural Sciences.

                Categories
                Subject: Original Article
                Custom metadata
                issue-copyright-statement © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                ScienceOpen disciplines: Genetics
                Keywords: bread wheat,micronutrients,gwas,multi-trait genomic prediction
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: bread wheat, micronutrients, gwas, multi-trait genomic prediction

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